The selection of optimal carbon capture technologies is paramount in enhancing the efficiency of carbon emission mitigation efforts. Due to the multifaceted nature of the influencing factors, a robust and systematic approach is essential for identifying the most effective solution. The present research introduces an innovative fuzzy multi-criteria decision-making framework developed to address these types of challenges. In the first phase, we introduce novel operational laws for \(p,q\) -quasirung orthopair fuzzy ( \(p,q\) -QOF) sets, thoroughly exploring their fundamental properties. Based on these operational laws, a set of advanced aggregation operators is developed, including the \(p,q\) -Quasirung Orthopair Fuzzy Weighted Exponential Averaging ( \(p,q\) -QOFWEA) operator and its dual counterpart, the \({\text{D}}_{(p,q)}\) QOFWEA operator, which significantly enhance decision-making capabilities. In the second phase, we extend the traditional entropy method to the \(p,q\) -QOF context for the determination of criteria weights and provide a comprehensive outline of the decision-making process. The effectiveness of the proposed approach is demonstrated through its application to a real-world case study focused on the selection of suitable carbon capture technologies. Numerical results highlight the superiority of the proposed method, yielding a prioritized list of carbon capture technologies with practical relevance to modern applications. This work offers a novel contribution by introducing the \(p,q\) -QOF framework and showcasing its potential for addressing complex decision-making problems in environmental technology selection.
